code: def dnorm_enet(
      X,
      theta,
      w,
      weights,
      X_sparse_scaling,
      positive,
      alpha=1.0,
      l1_ratio=1.0,
  ):
      """Theta should be centered."""
      X_dense, X_data, X_indices, X_indptr = _sparse_and_dense(X)
      skip = np.zeros(X.shape[1], dtype=np.int32)
      dnorm = dnorm_enet_cython(
          sparse.issparse(X),
          theta,
          w,
          X_dense,
          X_data,
          X_indices,
          X_indptr,
          skip,
          X_sparse_scaling,
          weights,
          X_sparse_scaling.any(),
          positive,
          alpha,
          l1_ratio,
      )
      return dnorm
  
file_path: celer/celer/homotopy.py
imports: import numpy
  from scipy import sparse
  from numpy.linalg import norm
  from sklearn.utils import check_array
  from sklearn.linear_model._base import _preprocess_data
  from .lasso_fast import celer
  from .group_fast import celer_grp, dnorm_grp
  from .cython_utils import compute_norms_X_col, compute_Xw
  from .cython_utils import dnorm_enet
  from .multitask_fast import celer_mtl
  from .PN_logreg import newton_celer
label_desc: Use image node_id 4 for calling a global function with example usage: dnorm_enet(X, theta, w, weights, X_sparse_scaling, positive, alpha, l1_ratio) and returns: dnorm